Probability matrix decomposition recommendation method based on user comments

Abstract

The invention provides a probability matrix decomposition recommendation method based on user comments, and particularly relates to the field of data mining. According to the method, probability matrix decomposition is enhanced through the user comment text, and the problems of data sparsity and cold start in a recommendation algorithm are solved. The method comprises the following steps: firstly, selecting user comments capable of representing project semantics as auxiliary information, and carrying out Word2vec word vector training and preprocessing on a comment text by utilizing an open source tool to obtain a project tag; and then a stack type noise reduction auto-encoder in deep learning is used to extract item features in the tag, and the stack type noise reduction auto-encoder converts sparse high-dimensional data into low-dimensional data, so that the robustness of a recommendation algorithm is enhanced. and finally, probability matrix decomposition is performed on the user score matrix and the extracted item matrix, and the item feature matrix extracted from the user comment text enhances the probability matrix decomposition, so that the recommendation effect is greatly improved.

Description

technical field [0001] The invention is a probability matrix decomposition recommendation method based on user comments, specifically relates to deep learning technology and a probability matrix decomposition method, and belongs to the field of data mining. Background technique [0002] In today's era, massive amounts of data are flooding the Internet, resulting in data redundancy. With the rapid development of computer technology, a large amount of data is utilized and applied in various fields of artificial intelligence to solve various needs of people's daily life. With the improvement of social living standards, people gradually tend to search for information and share their knowledge and life on the Internet, so recommendation algorithms emerge as the times require to query and recommend required information or services for users. [0003] Now the recommendation algorithm based on deep learning has become a research hotspot at home and abroad. Existing deep learning t...

AI Technical Summary

Problems solved by technology

The recommendation algorithm based on social network and trust, although these two kinds of auxiliary information can solve the user's cold start problem, it is still not helpful for the item's cold start

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